CITED2 Antibody

What are the optimal applications for CITED2 antibody detection?

Western blot, immunocytochemistry, and immunohistochemistry represent the primary validated applications for CITED2 antibody detection. For Western blot analysis, CITED2 typically appears as a band at approximately 30-35 kDa under reducing conditions . For immunocytochemistry, CITED2 exhibits primarily nuclear localization, requiring nuclear counterstaining (typically with DAPI) for accurate visualization . The antibody concentration should be optimized based on specific applications: typically 1 μg/mL for Western blot and 1-8 μg/mL for immunocytochemistry, depending on the specific antibody clone and cell type .

What sample preparation techniques optimize CITED2 detection in different experimental systems?

For optimal CITED2 detection:

• Western blot: Use PVDF membranes rather than nitrocellulose for improved protein retention. Process samples under reducing conditions using Immunoblot Buffer Group 1 for consistent results .

• Immunocytochemistry: Employ immersion fixation rather than cross-linking fixatives for better epitope preservation. For nuclear CITED2 visualization, counterstain with DAPI and use NorthernLights™ 557-conjugated secondary antibodies for optimal signal-to-noise ratio .

• Tissue sections: For paraffin-embedded sections, use antigen retrieval techniques before applying CITED2 antibodies at 3 μg/mL, incubating overnight at 4°C for best results .

How can I validate CITED2 antibody specificity for my experimental system?

Validation requires multiple approaches:

• Knockdown verification: Perform siRNA-mediated silencing of CITED2 (ideally testing multiple siRNAs as shown in research where si-1 and si-2 significantly reduced CITED2 at both mRNA and protein levels) .

• Expected localization pattern: Confirm nuclear localization in immunostaining, as CITED2 functions as a transcriptional cofactor .

• Multiple detection methods: Validate using both Western blot and immunostaining in the same experimental system.

• Positive controls: Use cell lines known to express CITED2, such as MCF-7, HeLa, C2C12, or NIH-3T3 cells .

How can CITED2 antibodies be applied in ChIP assays to study transcriptional regulation?

CITED2 functions as a transcriptional modulator, making ChIP (Chromatin Immunoprecipitation) a valuable technique for examining its genomic interactions. For optimal ChIP assay performance with CITED2 antibodies:

• Cross-linking protocol: Use 1% paraformaldehyde for protein-DNA complexes

• Sonication parameters: Aim for chromatin fragments of 300-500 base pairs

• Antibody selection: Use CITED2-specific antibodies (such as those from R&D Systems) with proper IgG controls

• Target validation: Quantify enrichment using qPCR normalized to IgG control (set as 1.0)

For example, research has successfully detected CITED2 binding to the hexokinase 1 promoter region at positions R2 (-2502 to -2284), R3 (-1655 to -1452), R6 (-831 to -609), and R7 (-472 to -365) relative to the transcription start site, demonstrating its direct regulation of metabolic genes .

What strategies can resolve contradictory CITED2 antibody detection patterns between different experimental contexts?

Contradictory results may arise from:

• Cell-type specific post-translational modifications: CITED2 function varies between cell types, potentially affecting epitope accessibility. Verify antibody performance in your specific cell type.

• Nuclear-cytoplasmic shuttling: CITED2 can shuttle between nuclear and cytoplasmic compartments depending on cellular conditions. Studies should include subcellular fractionation alongside total protein analysis .

• Isoform detection variability: Different antibodies may preferentially detect specific CITED2 isoforms. Employ antibodies targeting conserved regions when comparing across species or cell types.

• Expression level threshold effects: In cases of high CITED2 overexpression or near-complete knockdown, secondary effects on cell metabolism or viability may create artifacts. Include partial knockdown conditions to establish dose-dependent relationships .

How can researchers integrate multi-omics data with CITED2 antibody experiments to elucidate regulatory networks?

Effective integration strategies include:

• Correlation analysis between CITED2 protein levels and transcriptomics data: This can identify genes whose expression correlates with CITED2 abundance, suggesting potential regulatory relationships.

• CUT&RUN or CUT&Tag as alternatives to ChIP-seq: These techniques offer improved signal-to-noise ratios for transcription factor binding analysis, particularly valuable for CITED2's role as a transcriptional cofactor.

• Metabolic flux analysis combined with CITED2 manipulation: Given CITED2's role in metabolic reprogramming, measuring glycolysis and oxidative phosphorylation through ECAR and OCR assays following CITED2 modulation provides functional validation of regulatory effects .

• Pathway enrichment analysis: Applications like GSEA can identify signaling pathways associated with CITED2, as demonstrated in research showing CITED2's regulation of the AKT signaling pathway in TECs metabolism .

How should CITED2 antibodies be employed to investigate its role in cancer progression?

For cancer research applications:

• Tissue microarray (TMA) analysis: Compare CITED2 expression between tumor and adjacent normal tissues using standardized immunohistochemistry protocols (3 μg/mL antibody concentration, overnight incubation) .

• Metastasis models: Monitor CITED2 expression during epithelial-mesenchymal transition (EMT) by simultaneously examining CITED2 alongside EMT markers (E-cadherin, Vimentin, TWIST, Snail, N-Cadherin, ZEB1) .

• Protein complex analysis: Investigate CITED2's interactions with nucleolin, p300, and PRMT5, which form a regulatory complex in prostate cancer metastasis. Use co-immunoprecipitation with specific antibodies against each component .

• Functional assays: Following CITED2 knockdown or overexpression, assess proliferation, migration (wound healing assays), and invasion (transwell assays) to establish causality in cancer progression .

Research has shown CITED2 is highly expressed in metastatic prostate cancer, correlating with poor survival, and its knockdown inhibits cancer cell migration and metastasis in xenograft models .

What methodological approaches best capture CITED2's role in metabolic reprogramming during disease states?

To effectively study CITED2's metabolic regulatory functions:

• Extracellular flux analysis: Measure glycolytic parameters (ECAR) and mitochondrial respiration (OCR) using Seahorse technology after CITED2 manipulation. Key parameters to assess include:

  • Glycolysis, glycolytic capacity, and glycolytic reserve

  • Basal respiration, ATP production, maximal respiration, and spare respiratory capacity

• Metabolic enzyme expression analysis: Quantify expression of key metabolic enzymes regulated by CITED2:

  • PKM2, GLUT1, and LDHA as glycolytic markers

  • Mitochondrial complex components for oxidative metabolism

• Second messenger quantification: Measure cAMP levels, which show inverse correlation with CITED2 activity in metabolic regulation .

• Pathway manipulation: Use specific activators (e.g., SC79 for AKT pathway) to rescue metabolic phenotypes in CITED2-manipulated cells, establishing causality in regulatory relationships .

What considerations are critical when using CITED2 antibodies to study its role in inflammatory responses?

To accurately assess CITED2's role in inflammation:

• Temporal analysis: CITED2 expression changes dynamically during inflammatory responses. Design time-course experiments with consistent sampling intervals.

• Cell-specific expression patterns: Analyze CITED2 expression in specific cell populations within inflammatory microenvironments using flow cytometry or single-cell analysis.

• Inflammatory marker correlation: Simultaneously measure pro-inflammatory (CD86, IL-1β, iNOS, TNF-α) and anti-inflammatory (ARG1, IL-10) markers alongside CITED2 .

• Stimulus-specific responses: Compare CITED2 regulation under different inflammatory triggers (e.g., LPS, high glucose, hypoxia) as response patterns may differ .

Research demonstrates that CITED2 silencing attenuates inflammatory responses in sepsis-associated acute kidney injury models, while CITED2 overexpression exacerbates inflammation, suggesting its potential as a therapeutic target .

What are the most effective strategies for resolving non-specific binding with CITED2 antibodies?

To minimize non-specific binding:

• Blocking optimization: Test different blocking agents (5% non-fat milk, 5% BSA, commercial blockers) to determine optimal conditions for your specific antibody and experimental system .

• Antibody titration: Perform dilution series to identify the minimum effective concentration that maintains specific signal while reducing background.

• Secondary antibody selection: Match secondary antibodies precisely to the host species and isotype of your primary CITED2 antibody:

  • Use Anti-Rat IgG for MAB5005 (Rat IgG)

  • Use Anti-Sheep IgG for AF5005 (Sheep IgG)

• Cross-adsorbed secondary antibodies: When working with tissue samples or complex protein mixtures, use secondary antibodies that have been cross-adsorbed against potential cross-reactive species.

• Negative controls: Include samples where CITED2 is known to be absent or knockdown controls to establish baseline non-specific binding.

How can researchers distinguish between closely related CITED family proteins in their experiments?

The CITED protein family includes CITED1, CITED2, CITED3 (non-human), and CITED4, which share structural similarities. To ensure specificity:

• Epitope mapping: Use antibodies targeting unique regions of CITED2 rather than conserved domains shared with other family members.

• Western blot validation: CITED proteins have different molecular weights (CITED2: 30-35 kDa), allowing differentiation by size .

• Knockout/knockdown controls: Include CITED2-specific knockdown controls to verify antibody specificity.

• Parallel detection: When studying multiple CITED family members, run parallel assays with specific antibodies against each target.

• Recombinant protein controls: Use purified recombinant CITED proteins as positive controls to establish specificity across the family.

What considerations are important when interpreting quantitative data from CITED2 antibody experiments?

For accurate quantitative analysis:

• Loading control selection: Traditional housekeeping proteins may vary under conditions that alter CITED2. Consider multiple loading controls or total protein normalization methods.

• Linear detection range: Establish the linear detection range for your specific antibody and detection system to ensure quantification occurs within this range.

• Subcellular distribution effects: CITED2's nuclear-cytoplasmic shuttling may affect interpretation of total protein levels. Consider separate analysis of nuclear and cytoplasmic fractions.

• Post-translational modifications: CITED2 undergoes various modifications that may affect antibody binding. Consider using phospho-specific or modification-specific antibodies when relevant.

• Statistical analysis: For subtle changes in CITED2 levels, ensure adequate biological replicates (n≥3) and appropriate statistical tests for your experimental design .

How can machine learning approaches enhance CITED2 antibody-based research?

Machine learning can address several challenges in CITED2 research:

• Automated image analysis: For immunohistochemistry or immunocytochemistry data, convolutional neural networks can quantify CITED2 expression patterns across large datasets, reducing subjective interpretation.

• Literature mining: Natural language processing tools can systematically analyze published research on CITED2, identifying patterns and relationships not apparent through manual review.

• Pathway prediction: Machine learning algorithms can predict functional relationships between CITED2 and other proteins based on co-expression data, potentially identifying novel interaction partners.

• Antibody performance prediction: As demonstrated by platforms like BenchSci, machine learning can analyze published data to predict antibody performance in specific applications, even when catalog information is incomplete .

• Integrative multi-omics analysis: Advanced algorithms can integrate CITED2 protein expression data with transcriptomics, metabolomics, and phenotypic data to construct comprehensive regulatory networks.

What considerations are essential when designing longitudinal studies involving CITED2 antibody detection?

For effective longitudinal studies:

• Consistent sample processing: Standardize collection, storage, and processing protocols to minimize technical variation over time.

• Antibody lot consistency: Use the same antibody lot throughout the study or perform bridging studies between lots to ensure comparable detection.

• Internal controls: Include consistent positive and negative controls in each experimental batch to normalize between time points.

• Time-dependent experimental design: Consider circadian or other temporal variations in CITED2 expression when designing sampling timeframes.

• Sample preservation validation: For studies involving archived samples, verify that CITED2 epitopes remain stable under your storage conditions through time-course stability studies.

How can researchers effectively combine genetic manipulation with CITED2 antibody detection to establish causality?

To establish causal relationships:

• Rescue experiments: After CITED2 knockdown, reintroduce wild-type or mutant forms to determine which domains are essential for specific functions.

• Inducible expression systems: Use doxycycline-inducible or similar systems to control the timing and level of CITED2 expression, allowing dose-dependent and temporal analyses.

• Domain-specific mutations: Create point mutations in specific CITED2 domains to disrupt particular interactions (e.g., p300 binding) while preserving others.

• CRISPR-Cas9 approaches: Generate complete knockouts or specific mutations at the endogenous CITED2 locus, avoiding artifacts from overexpression systems.

• Animal models: Utilize conditional knockout mice to study tissue-specific functions of CITED2, with antibody detection confirming the pattern and efficiency of deletion .

Research demonstrates that CITED2 silencing inhibits the proliferation of HTR8-SVneo cells, with the degree of proliferation inhibition directly correlating with the efficiency of CITED2 knockdown .